Forecasting Freight Inspection Volume Using Bayesian Regularization Artificial Neural Networks: An Aggregation-Disaggregation Procedure

摘要

This study is focused on achieving a reliable prediction of the daily number of goods subject to inspection at Border Inspections Posts (BIPs). The final aim is to develop a prediction tool in order to aid the decision-making in the inspection process. The best artificial neural network (ANN) model was obtained by applying the Bayesian regularization approach. Furthermore, this study compares daily forecasting with a two-stage forecasting approach using a weekly aggregation-disaggregation procedure. The comparison was made using different performance indices. The BIP of the Port of Algeciras Bay was used as a case study. This approach may become a supporting tool for the prediction of the number of goods subject to inspection at other international inspection facilities.